AI Glossary: 3,885 terms, defined

Plain-language definitions of the vocabulary used across machine learning, model architectures, tooling and AI safety. Every entry opens with a one-sentence definition, then explains where the term is used.

3,885 terms

Computer Vision

Action Recognition

Action Recognition is the process of automatically identifying and interpreting human actions or activities from video or sensor data using computational methods.

Reinforcement Learning

Action-Value

Action-Value is a concept in reinforcement learning that represents the expected return or reward of taking a specific action in a given state.

Computer Vision

Activation Map

Activation map is a visual representation of neural network activations highlighting which parts of the input influence the model’s output.

Machine Learning

Active Learning

Active Learning is a machine learning approach where the algorithm selectively queries the most informative data points to improve its accuracy.

Other

Active Recall

Active Recall is a learning technique that involves actively stimulating memory during the learning process to enhance retention and understanding.

Computer Vision

Activity Recognition

Activity Recognition is the process of automatically identifying human actions or behaviors using data from sensors or devices.

Reinforcement Learning

Actor

An actor is a person who portrays characters in performances across theater, film, television, or other media.

Deep Learning & Neural Networks

Actor-Critic

Actor-Critic is a type of reinforcement learning architecture that combines two components: an actor, which makes decisions, and a critic, which evaluates those decisions.

Reinforcement Learning

Actuator

An actuator is a device that converts electrical, hydraulic, or pneumatic energy into physical motion to control or move mechanisms.

Generative AI

Ad Generation

Ad Generation is the automated process of creating digital advertisements using algorithms and data-driven techniques to optimize reach and engagement.

Machine Learning

AdaBoost

AdaBoost is a machine learning algorithm that combines multiple weak classifiers to create a strong classifier.

Machine Learning

Adagrad

Adagrad is an adaptive learning rate optimization algorithm used in training machine learning models.

Machine Learning

Adam Optimization

Adam Optimization is an adaptive learning rate optimization algorithm designed for training deep learning models.

Other

Adapter Model

Adapter Model is a technique in machine learning that efficiently fine-tunes pre-trained models by inserting small trainable modules, called adapters, without updating the entire model.

Other

Adaptive Logic

Adaptive Logic is a system that dynamically modifies its reasoning or decision-making processes based on changing inputs and environments.

Computer Vision

Adaptive Thresholding

Adaptive Thresholding is a dynamic image processing technique that determines threshold values locally for different regions of an image to effectively separate foreground from background.

Data & Analytics

Address Standardization

Address Standardization is the process of converting addresses into a consistent, uniform format to ensure accuracy and easy data management.

Machine Learning

Adjusted R-Squared

Adjusted R-Squared is a statistical measure that indicates how well a regression model fits the data, adjusted for the number of predictors used.

Other

Advanced Driver-Assistance System

Advanced Driver-Assistance System is a technology that enhances vehicle safety and driving ease by automatically assisting with tasks such as braking, steering, and lane keeping.

About the TiorAI AI Glossary

The TiorAI AI Glossary is a free reference of 3,885 artificial intelligence terms, each defined in plain English. Every entry opens with a one-sentence definition that stands on its own, then explains where the term is actually used and how it sits next to the ideas around it. There is nothing to sign up for and nothing to download: each definition has its own page and its own URL, so a term you needed once can be linked to and found again.

Three ways to find a term

They combine, and none of them cancels the others. The A to Z strip narrows the index to a single initial, with the # key collecting the entries that begin with a digit or a symbol and that no letter would reach. The category filter cuts the glossary to one of sixteen subject areas, among them Machine Learning, Deep Learning and Neural Networks, Natural Language Processing, Computer Vision, Generative AI, Reinforcement Learning, AI Agents and Systems, MLOps and Infrastructure, Data and Analytics, and AI Ethics. The search field matches names and definitions, so a concept you can only half-name still surfaces. Every combination has its own URL, which means a narrowed view can be bookmarked or sent to a colleague.

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What the definitions cover

The vocabulary of modern AI, at the three depths people arrive needing. The fundamentals a first week runs into — model, parameter, training, inference, overfitting. The architecture behind current systems — transformers, attention, embeddings, diffusion, retrieval-augmented generation. And the operational and governance language that turns up in a procurement review or a policy document — fine-tuning, hallucination, alignment, model drift, bias. Terms are defined as they are used in practice rather than as they appear in a paper abstract.

Definitions that answer first

Each entry leads with the answer. The opening sentence says what the term means without assuming the rest of the page, which is what makes an entry useful to someone who arrived from a search with exactly one question. The explanation underneath is for the reader who wants more than that: what the idea is commonly confused with, what it looks like in practice, and which neighbouring terms are worth reading next. The card in the index above shows the opening of that definition, so you can tell from the results whether an entry is the one you want.

Where the glossary fits

A glossary answers what something means; it is not the place that tells you what to do about it. Where a concept has products behind it, the AI tools directory is where they are listed, and where it has a working instruction behind it, the prompt library is where that lives. Entries link out to those when the link earns its place, and the glossary stays what a glossary is — the definitions, ordered so they can be browsed, and written so a single one can be read on its own.

Using this glossary

How is the glossary ordered?

Alphabetically by term name, which is the order a reference work is browsed in and the order every page of this archive has always used. The sort menu can switch it to reverse alphabetical, or to recently added and recently updated if you want to see what has changed.

What does the letter strip do?

It narrows the index to terms whose name begins with that character, and it keeps whatever else you have set — a category filter, a search, a sort — so you can move between letters without starting again. The # entry collects the terms that begin with a digit or a symbol, which no letter would reach.

Why do some terms have no specific category?

Entries that span several areas, or that predate the current category set, sit under “Other”. It holds a large share of the glossary, so the filter is most useful for narrowing to a named topic rather than for excluding one.

What is in a glossary entry?

A short definition in plain language, then a longer explanation of where the term is used and how it relates to neighbouring concepts. The card above shows the opening of that definition; the entry itself also links to related terms and, where relevant, to the AI tools that implement the idea.

How do I search the glossary?

The field at the top matches term names and definitions, so a concept you can only half-name will usually surface. Search combines with the category filter and the letter strip, and every combination has its own URL, so a narrowed view can be bookmarked or shared.